Impact of risk disclosure on the volatility, liquidity and performance of the UK and Canadian insurance companies
Bibliographic record
Abstract
Purpose This paper aims to investigate the impact of risk disclosure practices (voluntary, mandatory and risk disclosure index) on stock return volatility, market liquidity and financial performance for insurance companies in the UK and Canada, before and after the International Financial Reporting Standards (IFRS) adoption. Design/methodology/approach The panel data analysis covers 14 insurance companies in the UK and 12 in Canada over a six-year period, three years before and three years after the implementation of IFRS. The authors collected risk disclosure data manually from the annual reports and analyzed it through QSR NVivo software for each country. The other variables are secondary data collected from Thomson Reuters Eikon and Datastream. Findings The results reveal that mandatory risk disclosure practices positively influence stock return volatility for UK insurers but not Canadian ones. Moreover, both mandatory and voluntary risk disclosures increase market liquidity for UK insurers. The outcomes also show a negative influence of risk disclosure practices on financial performance for both the UK and Canadian insurers. The adoption of IFRS enhances the impact of risk disclosure practices in both countries on market liquidity and financial performance. Research limitations/implications The findings rationalize the impact of risk disclosure practices on volatility, liquidity and financial performance of UK and Canada insurers, and the effect of IFRS in triggering those results. Practical implications The findings highlight the diverse effects of voluntary and mandatory risk disclosure practices in enhancing market discipline and mitigating information asymmetry problems to investors. Regulators and policymakers could rely on the findings to amend and develop disclosure standards more frequently to assure their effectiveness. The authors also offer insights to managers to determine the levels of mandatory and voluntary disclosure practices and disclosure strategies to gain their stakeholders’ confidence. Originality/value This study contributes to the literature of risk disclosure in the insurance industry for both the UK and Canada where scarce studies are conducted. It also offers interesting implementations to investors, managers and policymakers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".